Wireless communications receivers typically use maximum likelihood sequence estimators (MLSE) or equalizers to address intersymbol interference caused by time dispersion of the wireless communications channels. Wireless communications channels may be characterized as time dispersive, frequency selective fading channels. The characteristics of such communications channels may change significantly during transmission of a message, such as messages sent via burst signals.
The communications channel is tracked during transmission of the burst signal so that good performance can be achieved. Consequently, many systems use some form of communications channel tracking that is intended to update a model of the communications channel during transmission of each burst signal to achieve better performance.
Variations in the burst signal can be directly related to the impulse response of the communications channel. The impulse response is a wideband channel characterization and contains information necessary to simulate or analyze any type of radio transmission through the communications channel. This stems from the fact that a communications channel may be modeled as a linear filter with a time varying impulse response. The filtering nature of the communications channel is caused by the summation of amplitudes and delays of the multiple arriving burst signals at any instant in time. The impulse response is a useful characterization of the communications channel.
Several adaptive type algorithms are commonly used for communications channel tracking in wireless communications receivers. These algorithms are used to periodically update the communications channel estimate during processing of the burst signal. The most common algorithms include least means squares (LMS) and recursive least squares (RLS). LMS based algorithms use an instantaneous approximation to the gradient of the optimization space. However, this makes LMS based algorithms more susceptible to noise and requires a large number of iterations to converge.
RLS based algorithms are known to have better convergence properties and are asymptotically optimal. The good convergence properties of the RLS algorithm is due to the use of information contained in the input data extending back to the instant of time when the algorithm was initiated. The recursive least squares algorithm starts with known initial conditions while using the information contained in new data samples to update old estimates. The resulting rate of convergence is usually an order of magnitude faster than the LMS algorithm. The improvement, however, is achieved at the expense of an increase in computational complexity over the LMS algorithm. Increased computational complexity also increases processing times.
With a burst signal, there is a limited amount of time for a communications receiver to identify and estimate the impulse response of the wireless communications channel, compensate for the effects of the wireless communications channel on the burst signal, and then decode and validate the data in the burst signal. This is particularly so when the burst signal is a networking waveform that is constrained by time division multiplexing of the RF resources, and processing of the burst signal needs to be completed in order to respond to the transmitter in a timely manner. The longer it takes to identify the wireless communications channel, the less time remains to decode and validate the data since there is a finite time to respond to the burst signal.
U.S. Pat. No. 7,050,513 discloses an approach for communications channel estimation where a channel tracking mechanism generates communications channel estimate updates based on blocks of samples during reception of a message. A weighted recursive least squares (RLS) algorithm implements the estimation process by recursively updating communications channel model parameters upon arrival of new sample data. The communications channel tracking updates channel estimate information once per sample block. An interblock exponential weighting factor is also applied. The block length is chosen short enough to enable good tracking performance while being sufficiently long enough to reduce the overhead of generating preliminary decisions and of updating precalculated tables in the equalizer.
Another approach is disclosed in U.S. Pat. No. 7,907,683 where a pilot-based communications channel estimation process includes receiving a signal that includes information bits transmitted in a wireless communications channel, executing the pilot-based communications channel estimation process having p structures for a vector of pilot structures and an upper bound N for a channel spread, and determining a result of a matrix inversion of a channel correlation matrix for an error channel estimation offline without performing a matrix inversion. Pilot information of the received signal is stored for channel recovery in a transform domain. The Toeplitz inverse is represented by a FFT representation. The process further includes detecting and estimating nonzero taps of a channel impulse response of the wireless communications channel, obtaining a non-structured minimum mean-square-error (MMSE) estimate as a first estimate of locations of the nonzero taps, and replacing the non-structured MMSE estimate by an estimate computed by a tap detection algorithm.
The above approaches for identifying wireless communications channels may still require large amounts of processing, which in turn, increases the processing times. Consequently, there is still a need to improve upon identifying wireless communications channels.